Electric chassis cooperative control method

Abstract

The invention relates to a cooperative control method for an electric chassis, which comprises the following steps of: analyzing coupling influence of different subsystems during operation, carding out influence factors causing contradictions among the different subsystems, and analyzing suspension-steering dynamics integrated control and suspension-braking dynamics integrated control, so as to realize cooperative control of the electric chassis. A complete integrated control framework is constructed by combining related theoretical knowledge of integrated control, and a final integrated control strategy is formulated according to specific control requirements, so that the integrated control has a better effect in the aspect of restraining the roll angle and the pitch angle of the automobile body, better stability can be maintained during turning of the automobile, the roll phenomenon is reduced, and the service life of the automobile is prolonged. The influence of transverse force on a vehicle body is effectively reduced, and meanwhile the driving safety and riding comfort of the vehicle during braking can be improved. In addition, coupling dynamics integrated control over suspension, braking and steering can be achieved, the vehicle is lightened, and the capacity of an intelligent driving decision-making system is powerfully enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative control of autonomous driving, and particularly to a collaborative control method for an electric chassis. Background Art

[0002] Compared with traditional internal combustion engine vehicles, electric vehicles have core advantages such as a significant reduction in power system noise, zero exhaust emissions, and flexible energy adaptation. They represent a revolutionary technological breakthrough. Their power sources can be compatible with nuclear energy, renewable energy, and a diversified clean energy system, effectively reducing the structural dependence of the transportation sector on fossil energy and establishing a complete technical closed-loop for clean energy substitution.

[0003] For electric vehicles, if each subsystem of the chassis is controlled separately, the limited control ability of a single subsystem and the different control methods of each subsystem will inevitably cause interference and conflicts in their respective systems during operation. For example, when the lateral force of the tire reaches the adhesion limit in the active steering system, the handling efficiency decays, resulting in the loss of lateral control. In the active braking system, longitudinal dynamic performance deterioration is likely to occur under emergency conditions. The quantization characteristic of the sudden change in braking torque may extend the braking distance and also cause coupling interference with the yaw stability control. The time-delay effect of the active suspension system deteriorates its dynamic coupling characteristics. Under the critical roll condition, the control timing mismatch caused by the response delay will seriously affect the anti-roll control. This will lead to the decline of the overall vehicle performance and unsatisfactory control effects. It is necessary to integrate and control each subsystem. The vehicle chassis integrated control that adopts the chassis integrated control strategy to ensure the coordinated operation of each subsystem of the vehicle and fully exploit the performance potential of the vehicle has become a research hotspot at home and abroad. Summary of the Invention

[0004] The purpose of the present invention is to solve the application problems in the integration of commercial vehicle electric chassis. Since the separate operation of a single subsystem can no longer meet the development requirements of current electric vehicles, and when each subsystem acts simultaneously, they will affect each other, resulting in a significant decline in the vehicle's performance, seriously damaging the vehicle's handling stability, ride comfort, and safety.

[0005] The method of the present invention is as follows:

[0006] A collaborative control method for an electric chassis, comprising the following steps:

[0007] S1. During the vehicle driving process, collect external scene road surface information and the vehicle body's own state information through sensors such as cameras and radars, and then calculate the wheel angle signal and output it to the ideal state vehicle model.

[0008] S2. Calculate the control output quantity by using the ideal state vehicle model and the inverse dynamics model.

[0009] S3. The fuzzy controller receives the vertical velocity signals of four body points of the actual vehicle, calculates the deviation E and the deviation change rate EC respectively with the vertical velocity signals of four body points of the vehicle model in the ideal state, and then performs fuzzification processing, fuzzy inference, and defuzzification processing in sequence, and outputs three fuzzy PID controller parameters, kp2, kd2, and kI2. Then, the active control force is output through the PID controller.

[0010] S4. After receiving the steering signal and the braking signal, the electric chassis integrated controller performs logical judgment and outputs the optimal control force to improve the vehicle handling stability and ride comfort.

[0011] Further, step S2 is specifically as follows:

[0012] S2.1. The vehicle model in the ideal state receives the steering angle signal and the braking opening signal, and outputs the yaw angular velocity, the lateral angle, and the vibration velocity signals of four body points.

[0013] S2.2. The input of the inverse dynamics model is the vehicle state signals such as the yaw angular velocity, and the output is the braking yaw moment, which is applied to the actual vehicle.

[0014] Further, step S3 is specifically as follows:

[0015] S3.1. The fuzzy controller receives the vertical velocity signals of four body points of the actual vehicle, and calculates the deviation E and the deviation change rate EC respectively with the vertical velocity signals of four body points of the reference model in the ideal state. Then, fuzzification processing is performed, that is, the membership degree is obtained according to the membership function. It is stipulated that the membership degree values NB, NM, NS, Z, PS, PM, and PB represent -3, -2, -1, 0, 1, 2, and 3 respectively, and the fuzzy subsets are divided.

[0016] Then, the membership degrees of the deviation E and the deviation change rate EC are respectively obtained according to the triangular membership function. At the same time, according to the principle that E and EC are closer to the endpoint values of the fuzzy subset interval, the membership degree values are determined.

[0017] S3.2. Fuzzy inference is performed based on the membership degrees of the deviation E and the deviation change rate EC, that is, the membership degree of the fuzzy output value U is obtained according to the fuzzy rule table, and the fuzzy output quantity is determined.

[0018] The membership degrees of the fuzzy output quantities kp2, kI2, and kd2 are still a fuzzy set, and defuzzification processing is performed on it, that is, an exact precise quantity is determined through the defuzzification method. The defuzzification method adopts the centroid method, and the control value is determined by calculating the centroid of the fuzzy output set.

[0019] S3.4. The PID controller receives the three fuzzy output values of kp2, kI2, and kd2, the deviation E, and the deviation change rate EC, and then calculates and outputs the active control force F based on the three coefficient parameters of kp2, kI2, and kd2, the deviation E, and the deviation change rate EC.

[0020] Further, step S4 is specifically as follows:

[0021] S4.1. The logic judgment module receives the steering signal, and its judgment rule is: when the steering angle is greater than 90°, the output is 1, otherwise it is 0.

[0022] S4.2. When the brake pedal opening is greater than 40%, the output is 1, otherwise it is 0, and its output result can only be 1 or 0.

[0023] S4.3. The logic judgment modules of steering and braking are output through the OR module.

[0024] S4.4. The Switch selection module makes a judgment according to the output value of the OR module, that is, when the output value of the OR module is equal to 0, it outputs the active force calculated by the S3.4 fuzzy PID controller, and when the output value of the OR module is equal to 1, it outputs the maximum active force (this given value should be measured through multiple experiments). Finally, it realizes the improvement of the vehicle's handling stability and ride comfort under emergency steering, braking, and combined working conditions.

[0025] Compared with the prior art, this patent application has the following beneficial effects:

[0026] 1. By analyzing the coupling effects existing during the operation of different subsystems, this invention sorts out the influencing factors that cause contradictions between different subsystems, analyzes the integrated control of suspension-steering dynamics and suspension-braking dynamics, and combines the relevant theoretical knowledge of integrated control to construct a complete integrated control architecture. According to specific control requirements, the final integrated control strategy is formulated, so that this integrated control has a good effect in suppressing the body roll angle and pitch angle, can maintain good stability when the vehicle turns, reduce the roll phenomenon, effectively reduce the influence of lateral force on the body, and at the same time improve the driving safety and ride comfort of the vehicle during braking.

[0027] 2. It can realize the integrated control of the coupling dynamics of suspension, braking, and steering, making the vehicle lightweight. At the same time, the integrated design significantly improves the speed and accuracy of intelligent driving decision-making, and effectively enhances the ability of the intelligent driving decision-making system.

[0028] 3. The integrated control performance of suspension-steering-braking significantly improves the vehicle's handling stability and ride comfort compared with the action of a single subsystem. Description of the Drawings

[0029] Figure 1Flowchart of the control method of the present invention Specific embodiments

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] As Figure 1 shown, the embodiment of the present invention provides an electric chassis cooperative control method, and the specific steps are as follows:

[0032] S1. During the vehicle driving process, collect the external scene road surface information and the vehicle's own state information through sensors such as cameras and radars, and then calculate the steering wheel angle signal and output it to the ideal state vehicle model.

[0033]

[0034] In the formula, δ is the steering angle required for the vehicle to travel from the current position to the preview point i, y i is the lateral offset required for the vehicle to travel from the current position to the preview point i, kp1 is the proportional coefficient, kd1 is the differential coefficient, and kI1 is the integral coefficient.

[0035] S2. Calculate the control output quantity by using the ideal state vehicle model and the inverse dynamics model.

[0036] S2.1. The ideal state vehicle model receives the steering angle signal and the brake opening signal, and outputs the yaw angular velocity, the lateral angle, and the vibration velocity signals of four body points.

[0037] The ideal state vehicle model is the transfer function of the yaw angular velocity r - steering angle δ:

[0038]

[0039] Steady-state gain G r As shown in Equation (3):

[0040]

[0041] The transfer function coefficient τ1 is as shown in Equation (4):

[0042]

[0043] The transfer function coefficient T1 is as shown in Equation (5):

[0044]

[0045] The transfer function coefficient T2 is shown in Equation (6):

[0046]

[0047] where M is the vehicle mass, I z is the yaw moment of inertia, r is the yaw angular velocity, δ is the steering wheel angle, K i is the cornering stiffness of the tire, V is the vehicle speed, x i is the distance from the center of mass to the i-th axis, and n is the number of vehicle axles. The equivalent distance x i ' from the center of mass to the i-th axis is shown in Equation (7):

[0048] x i ' = x i (1 + E i V 2 ) (7)

[0049] E i is calculated as shown in Equation (8):

[0050]

[0051] where M s is the sprung mass, K φ is the roll stiffness of the suspension, h is the distance from the center of mass of the sprung mass to the roll axis, φ is the roll angle, is the roll steer angle corresponding to per unit body roll angle, K ci is the camber thrust coefficient, is the camber angle generated by per unit body roll angle, and g is the gravitational constant.

[0052] S2.2. The input of the inverse dynamics model is vehicle state signals such as yaw angular velocity, and the output is the braking yaw moment, which is applied to the actual vehicle.

[0053] S3. The fuzzy controller receives the vertical velocity signals of four body points of the actual vehicle, calculates the deviation E and the deviation change rate EC respectively with the vertical velocity signals of four body points of the ideal state vehicle model, and then performs fuzzification processing, fuzzy inference, and defuzzification processing in sequence, and outputs three fuzzy PID controller parameters kp2, kd2, and kI2. Then, the active control force is output through the PID controller.

[0054] S3.1. The fuzzy controller receives the vertical velocity signals of four body points of the actual vehicle, and calculates the deviation E and the deviation change rate EC respectively with the vertical velocities of four body points of the ideal vehicle reference model. Then, fuzzification processing is performed, that is, the membership degree is obtained according to the membership function. It is stipulated that the membership degree values NB, NM, NS, Z, PS, PM, PB represent -3, -2, -1, 0, 1, 2, 3 respectively, and the fuzzy subsets are divided.

[0055] The triangular membership function is as follows:

[0056]

[0057] In the formula, x represents the deviation E and the rate of change of deviation EC.

[0058] Then, the membership degrees of the deviation E and the rate of change of deviation EC are respectively obtained according to the triangular membership function. At the same time, based on the principle that if E and EC are closer to the endpoint values of the fuzzy subset interval, they belong to that subset, the membership degree values are determined respectively.

[0059] S3.2. Perform fuzzy inference based on the membership degrees of the deviation E and the rate of change of deviation EC, that is, obtain the membership degree of the fuzzy output value U according to the fuzzy rule table and decide the fuzzy output quantity. The following Table 1 shows the formulated fuzzy control rule table.

[0060] Table 1 Fuzzy control rule table

[0061]

[0062] Use the membership degree values obtained in S3.1 to find the membership degree value of the fuzzy output quantity U through the fuzzy control rule, and find the membership degrees of the fuzzy output quantity U belonging to each fuzzy subset.

[0063] The fuzzy output quantity U is transformed into three fuzzy output quantities kp2, kI2, and kd2. First, the fuzzy subsets of the outputs kp2, kI2, and kd2 are divided respectively. It is stipulated that NB_P, NM_P, NS_P, Z_P, PS_P, PM_P, PB_P represent -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3 respectively; NB_I, NM_I, NS_I, Z_I, PS_I, PM_I, PB_I represent -0.06, -0.04, -0.02, 0, 0.02, 0.04, 0.06 respectively; NB_D, NM_D, NS_D, Z_D, PS_D, PM_D, PB_D represent -3, -2, -1, 0, 1, 2, 3 respectively. Then, according to the divided fuzzy subsets, the membership degrees of the three fuzzy output quantities kp2, kI2, and kd2 are calculated by multiplying the membership degree by the corresponding membership degree value respectively.

[0064] S3.3. The membership degrees of the fuzzy output quantities kp2, kI2, and kd2 are still a fuzzy set, and defuzzification is performed on it, that is, an exact precise quantity is determined through the defuzzification method. The defuzzification method adopts the centroid method, and the control value is determined by calculating the centroid of the fuzzy output set.

[0065] S3.4. The PID controller receives three fuzzy output values of \(k_{p2}\), \(k_{I2}\), \(k_{d2}\), the deviation \(E\), and the rate of change of deviation \(EC\), and then calculates and outputs the active control force \(F\) based on the three coefficient parameters of \(k_{p2}\), \(k_{I2}\), \(k_{d2}\), the deviation \(E\), and the rate of change of deviation \(EC\). The calculation formula for the active force of the PID controller is:

[0066] \(F = k_{p2}*E + k_{I2}*\int E + k_{d2}*EC\) (10)

[0067] In the formula, \(F\) is the active control force, \(k_{p2}\) is the proportional coefficient, \(k_{I2}\) is the integral coefficient, \(k_{d2}\) is the differential coefficient, \(E\) is the deviation, and \(EC\) is the rate of change of deviation.

[0068] Among them, the proportional link proportionally reflects the deviation signal of the control system. Once a deviation is formed, the controller immediately generates a control action to reduce the deviation. The integral loop is mainly used to eliminate the static error and improve the accuracy of the system without error. The strength of the integral action depends on the integral coefficient. The larger the integral coefficient, the weaker the integral action, and vice versa. The differential link reflects the change trend of the deviation signal. Its main function is to introduce an effective early correction signal into the system before the deviation signal exceeds the limit value, accelerate the action speed of the system, and reduce the adjustment time. Therefore, through the PID controller, the output value of the active control force can be adjusted with the change of the driving state, thereby regulating the vehicle body attitude and improving the vehicle performance.

[0069] S4. After receiving the steering signal and the braking signal, the electric chassis integrated controller performs logical judgment and outputs the optimal control force to improve the vehicle handling stability and ride comfort.

[0070] S4.1. The logic judgment module receives the steering signal, and its judgment rule is: when the steering angle is greater than 90°, the output is 1, otherwise it is 0.

[0071] S4.2. When the opening of the brake pedal is greater than 40%, the output is 1, otherwise it is 0, and its output result can only be 1 or 0.

[0072] S4.3. The logic judgment modules of steering and braking are output through the OR module.

[0073] S4.4. The Switch selection module makes a judgment according to the output value of the OR module, that is, when the output value of the OR module is equal to 0, it outputs the active force calculated by the S3.4 fuzzy PID controller, and when the output value of the OR module is equal to 1, it outputs the maximum active force (this given value should be obtained through multiple experiments). Finally, it realizes the improvement of the vehicle's handling stability and smoothness under emergency steering, braking, and combined working conditions.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An electric chassis collaborative control method, characterized in that It includes the following steps: S1. During the vehicle driving process, collect the external scene road surface information and the vehicle body's own state information through sensors such as cameras and radars, then calculate the wheel steering angle signal and output it to the ideal state vehicle model; S2. Calculate the control output quantity by using the ideal state vehicle model and the inverse dynamics model; S3. The fuzzy controller receives the vertical velocity signals of four vehicle body points of the actual vehicle, calculates the deviation E and the deviation change rate EC respectively with the vertical velocity signals of four vehicle body points of the ideal state vehicle model, and performs fuzzification processing, fuzzy inference, and defuzzification processing in sequence, and outputs three fuzzy PID controller parameters of kp2, kd2, and kI2; then, output the active control force through the PID controller; S4. After receiving the steering signal and the braking signal, the electric chassis integrated controller performs logical judgment and outputs the optimal control force to improve the vehicle handling stability and ride comfort.

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